电测与仪表2026，Vol.63Issue(1)：64-71,8.DOI:10.19753/j.issn1001-1390.2026.01.007
基于改进YOLOv7的输电线路机械外破隐患目标检测方法
Hidden target detection method for mechanical external damage of transmission line based on improved YOLOv7
摘要
Abstract
Aiming at the problem that the detection accuracy of hidden objects on mechanical external damage is not high,which is prone to appear false detection and missed detection in the case of complex background,large scale change and occlusion,a hidden target detection algorithm for mechanical external damage based on improved YOLOv7(you only look once version 7)is proposed.Firstly,the Swin Transformer attention mechanism is added to the Head to improve the extraction ability of multi-scale features.Then,some convolutions in the Backbone are re-placed by depth-wise separable convolution to reduce the operation cost of the model.While,the Focal-EIOU loss function is used to optimize the prediction box.Finally,the Mish activation function is introduced to enhance the generalization ability of the network,and improve the detection performance of the model when the complex back-ground and the object are partially obscured.The experimental results show that the improved algorithm is 5.2%,10.6%,and 5.2%higher than the original YOLOv7 in accuracy,recall,and mean average precision,respective-ly.Compared with other mainstream algorithms,it has obvious advantages in detection accuracy and model volume.The effectiveness of the improved method is verified,which provides algorithm support for edge recognition of me-chanical external damage hidden in complex backgrounds.关键词
YOLOv7/输电线路/机械外破/Swin Transformer注意力机制/Mish激活函数/Focal-EIOU损失函数Key words
YOLOv7/transmission line/mechanical external damage/Swin Transformer attention mechanism/Mi-sh activation function/Focal-EIOU loss function分类
信息技术与安全科学引用本文复制引用
王彦海,郭宸昕,吴德强..基于改进YOLOv7的输电线路机械外破隐患目标检测方法[J].电测与仪表,2026,63(1):64-71,8.基金项目
国家自然科学基金资助项目(52079070) （52079070）
